Large-Scale Machine Learning at Verizon: Theory and Applications

被引：0
|
作者：
Srivastava, Ashok [1 ]
机构：
[1] Verizon, Palo Alto, CA 94301 USA
来源：
KDD'16: PROCEEDINGS OF THE 22ND ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING | 2016年
关键词：
Large-scale machine learning; Orion; Revenue Generation;
D O I：
10.1145/2939672.2945361
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This talk will cover recent innovations in large-scale machine learning and their applications on massive, real-world data sets at Verizon. These applications power new revenue generating products and services for the company and are hosted on a massive computing and storage platform known as Orion. We will discuss the architecture of Orion and the underlying algorithmic framework. We will also cover some of the real world aspects of building a new organization dedicated to creating new product lines based on data science.
引用
收藏
页码：417 / 417
页数：1
相关论文
共 50 条
  • [21] Compressed linear algebra for large-scale machine learning
    Ahmed Elgohary
    Matthias Boehm
    Peter J. Haas
    Frederick R. Reiss
    Berthold Reinwald
    The VLDB Journal, 2018, 27 : 719 - 744
  • [22] A review of Nystrom methods for large-scale machine learning
    Sun, Shiliang
    Zhao, Jing
    Zhu, Jiang
    INFORMATION FUSION, 2015, 26 : 36 - 48
  • [23] Introduction to Special Issue on Large-Scale Machine Learning
    Hsu, Chun-Nan
    ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY, 2011, 2 (03)
  • [24] Large-scale machine learning for metagenomics sequence classification
    Vervier, Kevin
    Mahe, Pierre
    Tournoud, Maud
    Veyrieras, Jean-Baptiste
    Vert, Jean-Philippe
    BIOINFORMATICS, 2016, 32 (07) : 1023 - 1032
  • [25] Large-Scale Strategic Games and Adversarial Machine Learning
    Alpcan, Tansu
    Rubinstein, Benjamin I. P.
    Leckie, Christopher
    2016 IEEE 55TH CONFERENCE ON DECISION AND CONTROL (CDC), 2016, : 4420 - 4426
  • [26] Dynamic Control Flow in Large-Scale Machine Learning
    Yu, Yuan
    Abadi, Martin
    Barham, Paul
    Brevdo, Eugene
    Burrows, Mike
    Davis, Andy
    Dean, Jeff
    Ghemawat, Sanjay
    Harley, Tim
    Hawkins, Peter
    Isard, Michael
    Kudlur, Manjunath
    Monga, Rajat
    Murray, Derek
    Zheng, Xiaoqiang
    EUROSYS '18: PROCEEDINGS OF THE THIRTEENTH EUROSYS CONFERENCE, 2018,
  • [27] Large-Scale Machine Learning Approaches for Molecular Biophysics
    Ramanathan, Arvind
    Chennubhotla, Chakra S.
    Agarwal, Pratul K.
    Stanley, Christopher B.
    BIOPHYSICAL JOURNAL, 2015, 108 (02) : 370A - 370A
  • [28] Large-Scale Machine Learning with Stochastic Gradient Descent
    Bottou, Leon
    COMPSTAT'2010: 19TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL STATISTICS, 2010, : 177 - 186
  • [29] Compressed linear algebra for large-scale machine learning
    Elgohary, Ahmed
    Boehm, Matthias
    Haas, Peter J.
    Reiss, Frederick R.
    Reinwald, Berthold
    VLDB JOURNAL, 2018, 27 (05): : 719 - 744
  • [30] Quick extreme learning machine for large-scale classification
    Albtoush, Audi
    Fernandez-Delgado, Manuel
    Cernadas, Eva
    Barro, Senen
    NEURAL COMPUTING & APPLICATIONS, 2022, 34 (08): : 5923 - 5938
12345